AI application areas in logistics: Solution to the shortage of skilled workers?

The trending topics that the logistics industry faces include digitalization of supply chains and internal processes, artificial intelligence (AI) as well as autonomous driving and scheduling. Find out how AI is changing the logistics industry, what specific applications there are in the logistics sector and what solutions AI can offer in the fight against the increasing shortage of skilled workers.

In brief

Artificial intelligence makes supply chains controllable and transparent.
AI significantly changes many jobs in logistics.
In the medium term, autonomous driving will remain limited to depots.
Artificial intelligence was a top issue at transport logistic 2019.

AI significantly changes jobs in logistics

Our human brain has about 100 billion interconnected neurons that transmit information by generating electrical signals. This enables the human being to learn, to draw conclusions and to think abstractly. In artificial intelligence, the neurons are replaced by artificial neurons and are trained with the help of algorithms.

The technology has the potential to significantly change many jobs in logistics.

Rummel, Stefan

Managing Director
Messe München

Human intelligence is not simulated, but “training data is harmonized, aggregated and fed into a framework for machine learning,” explains Sara Van Gelder, who is responsible for developing the freight business at Brussels Airport.

That's how it works: Based on the training data, machine learning helps to train a pattern recognition. This saves companies the manual creation of a model and the associated effort, such as defining rules, checks and interpretations. The quality of the training data is decisive for success.

The advantage: AI leads to automated, highly flexible logistics

For the logistics industry, AI offers much more than just the mere optimization of existing operations. AI makes supply chains more controllable and transparent. Van Gelder explains: “AI enables us to act with foresight, for example in the areas of

capacity management,
route planning,
network planning and
risk management.”

In addition, "offers can be tailored more precisely to the needs of the customer, while logistics becomes an integral part of automated and highly flexible industrial production and trade,” emphasizes Tim Schneider, who is responsible for processes, standards and digitalization at the German Federal Association for Freight Forwarding and Logistics (DSLV). “It is important to share data and understand it as a valuable source of information,” Schneider continues. Providing information in real time plays a decisive role here.

Artificial intelligence: areas of application in logistics

Concrete AI applications for logistics can be found, for example, in the airfreight industry: “This is where algorithms are developed that can learn and optimize the stacking of freight pallets,” explains Sara Van Gelder.

Examples can also be found in the field of transport management systems:

Solutions such as Opheo from initions AG have an artificial planning intelligence that allows predictions about possible transport delays in the future.
The software and consulting company Soloplan provides another example: its transport management system CarLo has been equipped with a powerful algorithm that can “learn” the behavior of dispatchers. On this basis, the solution creates a model that allows autonomous planning of future tours, considering the learned rules.

Machine learning makes transport planning faster, less error-prone and more effective. Another plus is that knowledge can no longer be lost in the event of staff changes. Because algorithms learn the behavior of dispatchers: a new dispatcher will also be able to plan the tours in the same way as a long-time employee, because the dispatcher software acquired the behavior based on the training data and can thus offer valuable support.

Autonomous driving: truck drivers remain an integral part of the supply chain

Another field of application of artificial intelligence is autonomous driving. Manufacturers are currently intensively working on assistance and control systems to make autonomous vehicles ready for series production in the coming years. In the medium term, assistance systems will control vehicles on motorways, and, in the long term, many other functions will simplify the work behind the wheel.

“In the long term, truck drivers will, however, remain an integral part of the supply chain,” explains Wolfgang Inninger, Head of Project Center Traffic, Mobility and Environment at the Fraunhofer Institute for Material Flow and Logistics IML. Hence, autonomous driving is not a solution for the increasing lack of truck drivers.

However, it can offer other advantages: “Autonomous trucks in depots offer a high savings potential,” continues Inninger. If it were possible for truck drivers to use the time spent on the premises as breaks, the effective driving time of the truck driver could be optimized.

This opinion is also underlined by a study by the Automotive Technology Research Association (FAT) within the German Association of the Automotive Industry (VDA). This study sees further economic potential, for example, in the fact that the driver takes over another, already loaded vehicle directly after arrival and can immediately start the return journey. But even for this scenario there are still some organizational challenges to be solved.

Further information

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